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Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            Legal Judgment Prediction (LJP) aims to automatically predict a law case\u2019s judgment results based on the text description of its facts. In practice, the confusing law articles (or charges) problem frequently occurs, reflecting that the law cases applicable to similar articles (or charges) tend to be misjudged. Although some recent works based on prior knowledge solve this issue well, they ignore that confusion also occurs between law articles with a high posterior semantic similarity due to the data imbalance problem instead of only between the prior highly similar ones, which is this work\u2019s further finding. This article proposes an end-to-end model named\n            <jats:italic>D-LADAN<\/jats:italic>\n            to solve the above challenges. On the one hand, D-LADAN constructs a graph among law articles based on their text definition and proposes a graph distillation operator (GDO) to distinguish the ones with a high prior semantic similarity. On the other hand, D-LADAN presents a novel momentum-updated memory mechanism to dynamically sense the posterior similarity between law articles (or charges) and a weighted GDO to adaptively capture the distinctions for revising the inductive bias caused by the data imbalance problem. We perform extensive experiments to demonstrate that D-LADAN significantly outperforms state-of-the-art methods in accuracy and robustness.\n          <\/jats:p>","DOI":"10.1145\/3689628","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T12:34:33Z","timestamp":1724502873000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6902-2572","authenticated-orcid":false,"given":"Nuo","family":"Xu","sequence":"first","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1434-837X","authenticated-orcid":false,"given":"Pinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-8248","authenticated-orcid":false,"given":"Junzhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0908-0559","authenticated-orcid":false,"given":"Feiyang","family":"Sun","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7363-1143","authenticated-orcid":false,"given":"Lin","family":"Lan","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3911-4260","authenticated-orcid":false,"given":"Jing","family":"Tao","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0424-9845","authenticated-orcid":false,"given":"Li","family":"Pan","sequence":"additional","affiliation":[{"name":"Institute of Cyber Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-0362","authenticated-orcid":false,"given":"Xiaohong","family":"Guan","sequence":"additional","affiliation":[{"name":"MOE KLINNS Lab, Xi\u2019an Jiaotong University, Xi\u2019an, China and Department of Automation and NLIST Lab, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat Red Avila Igor Babuschkin Suchir Balaji Valerie Balcom Paul Baltescu Haiming Bao Mohammad Bavarian Jeff Belgum Irwan Bello Jake Berdine Gabriel Bernadett-Shapiro Christopher Berner Lenny Bogdonoff Oleg Boiko Madelaine Boyd Anna-Luisa Brakman Greg Brockman Tim Brooks Miles Brundage Kevin Button Trevor Cai Rosie Campbell Andrew Cann Brittany Carey Chelsea Carlson Rory Carmichael Brooke Chan Che Chang Fotis Chantzis Derek Chen Sully Chen Ruby Chen Jason Chen Mark Chen Ben Chess Chester Cho Casey Chu Hyung Won Chung Dave Cummings Jeremiah Currier Yunxing Dai Cory Decareaux Thomas Degry Noah Deutsch Damien Deville Arka Dhar David Dohan Steve Dowling Sheila Dunning Adrien Ecoffet Atty Eleti Tyna Eloundou David Farhi Liam Fedus Niko Felix Sim\u00f3n Posada Fishman Juston Forte Isabella Fulford Leo Gao Elie Georges Christian Gibson Vik Goel Tarun Gogineni Gabriel Goh Rapha Gontijo-Lopes Jonathan Gordon Morgan Grafstein Scott Gray Ryan Greene Joshua Gross Shixiang Shane Gu Yufei Guo Chris Hallacy Jesse Han Jeff Harris Yuchen He Mike Heaton Johannes Heidecke Chris Hesse Alan Hickey Wade Hickey Peter Hoeschele Brandon Houghton Kenny Hsu Shengli Hu Xin Hu Joost Huizinga Shantanu Jain and Shawn Jain. 2023. 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